Applied AI + Infrastructure

David Braun

AI Systems Engineerbuilding automation platforms

I design and build systems that combine AI, workflow automation, and infrastructure to solve real operational problems.

The work is systems-first: architecture, orchestration, validation, state, and delivery paths that turn AI capability into usable software.

Examples of systems I build

AI-powered automation platforms

retrieval and knowledge systems

workflow automation tools

production AI infrastructure

Maple Grove, MinnesotaOpen to full-time roles, consulting, and selected systems work

System Lens

AI is one layer in the architecture

Framing

problemsystemoutcome

Layer 1

Workflow Design

Start with the operational path: how requests enter the system, how tasks are routed, and where humans or downstream services need clean handoffs.

Layer 2

AI Application Layer

Use models inside structured pipelines for retrieval, classification, generation, or guidance, with validation around the output instead of trust by default.

Layer 3

Infrastructure and State

Back the workflow with queues, persistence, observability, and service boundaries so the system can be operated like software instead of a demo.

Operating Pattern

systems first

+ automation platforms

+ retrieval systems

+ workflow tooling

+ production infrastructure

What I Build

Systems that combine AI, workflow automation, infrastructure, and domain-specific software.

I build systems that use AI inside real operational workflows. The work usually combines applied AI, workflow automation, infrastructure, and domain-specific software into one system that people can actually run.

The interesting part is rarely the model alone. It is the architecture around the model: how information enters the system, how decisions are validated, how state is persisted, and how useful output reaches a human or downstream workflow.

I use AI to accelerate system exploration and scaffolding, but the real work happens in engineering rigor, architecture design, and refining systems until they operate reliably.

Engineering Philosophy

The breadth across voice workflows, legal intake, planning engines, and lecture pipelines is intentional. I explore adjacent domains to find high-leverage operational problems, then go deeper where the system design work is strongest.

Applied AI

Use models for retrieval, classification, generation, or guidance only when they fit inside a controlled system boundary.

Workflow Automation

Reduce manual routing, triage, follow-up, and review work by giving the system an explicit operational path.

Infrastructure

Treat queues, persistence, deployment, observability, and failure handling as part of the product surface.

Operational Software

Build software that people can operate, inspect, and trust in real environments rather than demo-only interfaces.

Selected Systems

Five systems that best represent how I combine applied AI, workflow software, and production-minded engineering.

Case 01Applied AI & Automation SystemsActive Build

StormIQ

AI-powered lead generation platform designed to automate prospect engagement workflows and move structured outcomes into sales operations.

My Role

Sole architect and full-stack engineer

Core Constraint

Async job orchestration: separating telephony events from decision logic via queue-backed services

Outcome

Architecture validated with working voice gateway, queue orchestration, and CRM integration layer; advancing toward pilot deployment

Problem

Lead generation teams lose momentum when telephony, qualification, and follow-up depend on scripts, disconnected tools, and inconsistent operator decisions.

Evidence

The architecture is documented as a real voice workflow system with clear boundaries between intake, orchestration, decisioning, and delivery.

Architecture diagramWorkflow diagramLead routing artifacts

How I Build

Architecture first, AI as leverage, engineering as the finishing discipline.

Architecture-first workflow design, AI-assisted scaffolding, and then a refinement pass to make the system operational and reliable.

Step 1

Architecture-first design

Start with the workflow, failure points, and system boundaries before optimizing implementation details.

Step 2

AI-assisted scaffolding

Use AI to accelerate exploration, interface drafts, and early system structure without confusing speed for finished engineering.

Step 3

Engineering refinement

Refine architecture, algorithms, validation, and reliability by hand until the system behaves like software people can trust.

Step 4

Automation focus

Prioritize systems that remove manual routing, triage, and repetitive coordination from real operational workflows.

Builder Mindset

Fast initial momentum is useful only when the architecture survives contact with reality.

I use AI to compress the time between idea and working structure. Then I tighten the system by making the boundaries explicit, simplifying the workflow, and keeping the operational path understandable for humans.

Map the workflow before optimizing the implementation.

Keep policy, routing, and state visible in the architecture.

Prefer systems that reduce manual coordination instead of adding novelty.

Preferred Problem Shape

Systems that need routing, validation, orchestration, and an actual delivery path to a user, operator, or downstream business workflow.

Engineering Principles

The constraints I try to preserve while moving quickly.

A small set of constraints: solve real problems, keep systems inspectable, use AI as leverage, and simplify until the architecture is dependable.

Solve real problems

The goal is not novelty. I optimize for systems that improve how work actually gets done.

Build systems, not features

Features matter less than the architecture that makes them dependable, inspectable, and extensible.

Use AI as leverage

AI is most useful when it speeds up architecture, implementation, and workflows inside a well-structured system.

Simplify complex systems

Prefer explicit boundaries, readable flows, and controlled operational surfaces over unnecessary complexity.

Current Interests

The problem spaces I keep returning to.

Mostly applied AI systems, workflow automation, operational software, and infrastructure-backed products where reliability matters as much as speed.

Applied AI systems

Designing systems where models are only one layer inside a larger operational architecture.

Workflow automation

Reducing manual decision paths with queues, policy engines, retrieval, and human-review seams.

Operational software

Building software that coordinates real-world work rather than isolated toy interactions.

Infrastructure-backed platforms

Treating deployment, observability, and data movement as part of the product surface from day one.

Get In Touch

For systems work where architecture, implementation, and product judgment all need to show up in the same project.

Start a conversation

If you need someone who can turn complex ideas into working systems, automate real workflows, and build with both speed and engineering rigor, let's talk. I'm open to full-time roles, consulting engagements, and selected builds.

View experience, skills, and credentials

davidjbraun777@gmail.com

Location

Maple Grove, Minnesota

Quick chat

Preferred

Book a 15-minute meeting

Best for role fit, system scope, or project next steps.

Profiles